Instructions to use Qwen/Qwen3-Reranker-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen3-Reranker-8B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Reranker-8B") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Reranker-8B") - sentence-transformers
How to use Qwen/Qwen3-Reranker-8B with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Qwen/Qwen3-Reranker-8B") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Howto create a FP8 quant?
#8
by JochenGebhard - opened
Hello all,
it was easy to create a FP8 quant of the 8b-Embedding Model. The creation of a quant for the Reranker failed for me using Llmcompressor...
The result is technically loadable, but the result of the reranking is always 0.50. Does anybody of you can share the receipt or code to create a FP8 quant of this Model?
Thanks a lot and happy new year π